Bibliographic record
Abstract
This paper explores a culture and a research method, based on the conjecture that common similes and metaphors in a culture for “family” may offer insights into important aspects of the meanings, values, and ideals connected to family in that culture. With a focus on China, we looked for the first similes and metaphors for family that came up on the two most popular Chinese search engines, Baidu and Google. We winnowed the first hits, eliminating those that were not similes and metaphors and those that were to websites that few other websites linked to. In the end, we had nine Chinese similes and metaphors for family. They include: Family is a gentle harbor, a harbor for all seasons, a haven or refuge, a gas station, the center of the earth, and a little wooden boat on the river. We believe that these figures of speech represent Chinese cultural values that are important to Chinese thinking about families. Included in that, the figures of speech seem to us to represent the centrality of family in a society where for many the help they need will have to come from family. The method of investigating similes and metaphors for family as a way of understanding family in a culture has its risks, including issues of whose reality is reflected on websites and how search engines give priority to what comes up first in a search. But the method also seems worth considering as an addition to other social science tools for illuminating aspects of family life in a culture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".